€•}„Œsphinx.addnodes”Œdocument”“”)”}”(Œ rawsource”Œ”Œchildren”]”(Œ translations”Œ LanguagesNode”“”)”}”(hhh]”(hŒ pending_xref”“”)”}”(hhh]”Œdocutils.nodes”ŒText”“”ŒChinese (Simplified)”…””}”Œparent”hsbaŒ attributes”}”(Œids”]”Œclasses”]”Œnames”]”Œdupnames”]”Œbackrefs”]”Œ refdomain”Œstd”Œreftype”Œdoc”Œ reftarget”Œ@/translations/zh_CN/mm/damon/monitoring_intervals_tuning_example”Œmodname”NŒ classname”NŒ refexplicit”ˆuŒtagname”hhh ubh)”}”(hhh]”hŒChinese (Traditional)”…””}”hh2sbah}”(h]”h ]”h"]”h$]”h&]”Œ refdomain”h)Œreftype”h+Œ reftarget”Œ@/translations/zh_TW/mm/damon/monitoring_intervals_tuning_example”Œmodname”NŒ classname”NŒ refexplicit”ˆuh1hhh ubh)”}”(hhh]”hŒItalian”…””}”hhFsbah}”(h]”h ]”h"]”h$]”h&]”Œ refdomain”h)Œreftype”h+Œ reftarget”Œ@/translations/it_IT/mm/damon/monitoring_intervals_tuning_example”Œmodname”NŒ classname”NŒ refexplicit”ˆuh1hhh ubh)”}”(hhh]”hŒJapanese”…””}”hhZsbah}”(h]”h ]”h"]”h$]”h&]”Œ refdomain”h)Œreftype”h+Œ reftarget”Œ@/translations/ja_JP/mm/damon/monitoring_intervals_tuning_example”Œmodname”NŒ classname”NŒ refexplicit”ˆuh1hhh ubh)”}”(hhh]”hŒKorean”…””}”hhnsbah}”(h]”h ]”h"]”h$]”h&]”Œ refdomain”h)Œreftype”h+Œ reftarget”Œ@/translations/ko_KR/mm/damon/monitoring_intervals_tuning_example”Œmodname”NŒ classname”NŒ refexplicit”ˆuh1hhh ubh)”}”(hhh]”hŒSpanish”…””}”hh‚sbah}”(h]”h ]”h"]”h$]”h&]”Œ refdomain”h)Œreftype”h+Œ reftarget”Œ@/translations/sp_SP/mm/damon/monitoring_intervals_tuning_example”Œmodname”NŒ classname”NŒ refexplicit”ˆuh1hhh ubeh}”(h]”h ]”h"]”h$]”h&]”Œcurrent_language”ŒEnglish”uh1h hhŒ _document”hŒsource”NŒline”NubhŒcomment”“”)”}”(hŒ SPDX-License-Identifier: GPL-2.0”h]”hŒ SPDX-License-Identifier: GPL-2.0”…””}”hh£sbah}”(h]”h ]”h"]”h$]”h&]”Œ xml:space”Œpreserve”uh1h¡hhhžhhŸŒZ/var/lib/git/docbuild/linux/Documentation/mm/damon/monitoring_intervals_tuning_example.rst”h KubhŒsection”“”)”}”(hhh]”(hŒtitle”“”)”}”(hŒ1DAMON Moniting Interval Parameters Tuning Example”h]”hŒ1DAMON Moniting Interval Parameters Tuning Example”…””}”(hh»hžhhŸNh Nubah}”(h]”h ]”h"]”h$]”h&]”uh1h¹hh¶hžhhŸh³h KubhŒ paragraph”“”)”}”(hŒóDAMON's monitoring parameters need tuning based on given workload and the monitoring purpose. There is a :ref:`tuning guide ` for that. This document provides an example tuning based on the guide.”h]”(hŒlDAMON’s monitoring parameters need tuning based on given workload and the monitoring purpose. There is a ”…””}”(hhËhžhhŸNh Nubh)”}”(hŒA:ref:`tuning guide `”h]”hŒinline”“”)”}”(hhÕh]”hŒ tuning guide”…””}”(hhÙhžhhŸNh Nubah}”(h]”h ]”(Œxref”Œstd”Œstd-ref”eh"]”h$]”h&]”uh1h×hhÓubah}”(h]”h ]”h"]”h$]”h&]”Œrefdoc”Œ,mm/damon/monitoring_intervals_tuning_example”Œ refdomain”häŒreftype”Œref”Œ refexplicit”ˆŒrefwarn”ˆŒ reftarget”Œ+damon_design_monitoring_params_tuning_guide”uh1hhŸh³h KhhËubhŒH for that. This document provides an example tuning based on the guide.”…””}”(hhËhžhhŸNh Nubeh}”(h]”h ]”h"]”h$]”h&]”uh1hÉhŸh³h Khh¶hžhubhµ)”}”(hhh]”(hº)”}”(hŒSetup”h]”hŒSetup”…””}”(hjhžhhŸNh Nubah}”(h]”h ]”h"]”h$]”h&]”uh1h¹hjhžhhŸh³h K ubhÊ)”}”(hŒÿFor below example, DAMON of Linux kernel v6.11 and `damo `_ (DAMON user-space tool) v2.5.9 was used to monitor and visualize access patterns on the physical address space of a system running a real-world server workload.”h]”(hŒ3For below example, DAMON of Linux kernel v6.11 and ”…””}”(hjhžhhŸNh NubhŒ reference”“”)”}”(hŒ+`damo `_”h]”hŒdamo”…””}”(hjhžhhŸNh Nubah}”(h]”h ]”h"]”h$]”h&]”Œname”Œdamo”Œrefuri”Œ!https://github.com/damonitor/damo”uh1jhjubhŒtarget”“”)”}”(hŒ$ ”h]”h}”(h]”Œdamo”ah ]”h"]”Œdamo”ah$]”h&]”Œrefuri”j.uh1j/Œ referenced”KhjubhŒ¡ (DAMON user-space tool) v2.5.9 was used to monitor and visualize access patterns on the physical address space of a system running a real-world server workload.”…””}”(hjhžhhŸNh Nubeh}”(h]”h ]”h"]”h$]”h&]”uh1hÉhŸh³h Khjhžhubeh}”(h]”Œsetup”ah ]”h"]”Œsetup”ah$]”h&]”uh1h´hh¶hžhhŸh³h K ubhµ)”}”(hhh]”(hº)”}”(hŒ'5ms/100ms intervals: Too Short Interval”h]”hŒ'5ms/100ms intervals: Too Short Interval”…””}”(hjThžhhŸNh Nubah}”(h]”h ]”h"]”h$]”h&]”uh1h¹hjQhžhhŸh³h KubhÊ)”}”(hXyLet's start by capturing the access pattern snapshot on the physical address space of the system using DAMON, with the default interval parameters (5 milliseconds and 100 milliseconds for the sampling and the aggregation intervals, respectively). Wait ten minutes between the start of DAMON and the capturing of the snapshot, to show a meaningful time-wise access patterns. ::”h]”hXxLet’s start by capturing the access pattern snapshot on the physical address space of the system using DAMON, with the default interval parameters (5 milliseconds and 100 milliseconds for the sampling and the aggregation intervals, respectively). Wait ten minutes between the start of DAMON and the capturing of the snapshot, to show a meaningful time-wise access patterns.”…””}”(hjbhžhhŸNh Nubah}”(h]”h ]”h"]”h$]”h&]”uh1hÉhŸh³h KhjQhžhubhŒ literal_block”“”)”}”(hŒA# damo start # sleep 600 # damo record --snapshot 0 1 # damo stop”h]”hŒA# damo start # sleep 600 # damo record --snapshot 0 1 # damo stop”…””}”hjrsbah}”(h]”h ]”h"]”h$]”h&]”h±h²uh1jphŸh³h KhjQhžhubhÊ)”}”(hX`Then, list the DAMON-found regions of different access patterns, sorted by the "access temperature". "Access temperature" is a metric representing the access-hotness of a region. It is calculated as a weighted sum of the access frequency and the age of the region. If the access frequency is 0 %, the temperature is multiplied by minus one. That is, if a region is not accessed, it gets minus temperature and it gets lower as not accessed for longer time. The sorting is in temperature-ascendint order, so the region at the top of the list is the coldest, and the one at the bottom is the hottest one. ::”h]”hXeThen, list the DAMON-found regions of different access patterns, sorted by the “access temperatureâ€. “Access temperature†is a metric representing the access-hotness of a region. It is calculated as a weighted sum of the access frequency and the age of the region. If the access frequency is 0 %, the temperature is multiplied by minus one. That is, if a region is not accessed, it gets minus temperature and it gets lower as not accessed for longer time. The sorting is in temperature-ascendint order, so the region at the top of the list is the coldest, and the one at the bottom is the hottest one.”…””}”(hj€hžhhŸNh Nubah}”(h]”h ]”h"]”h$]”h&]”uh1hÉhŸh³h K#hjQhžhubjq)”}”(hX## damo report access --sort_regions_by temperature 0 addr 16.052 GiB size 5.985 GiB access 0 % age 5.900 s # coldest 1 addr 22.037 GiB size 6.029 GiB access 0 % age 5.300 s 2 addr 28.065 GiB size 6.045 GiB access 0 % age 5.200 s 3 addr 10.069 GiB size 5.983 GiB access 0 % age 4.500 s 4 addr 4.000 GiB size 6.069 GiB access 0 % age 4.400 s 5 addr 62.008 GiB size 3.992 GiB access 0 % age 3.700 s 6 addr 56.795 GiB size 5.213 GiB access 0 % age 3.300 s 7 addr 39.393 GiB size 6.096 GiB access 0 % age 2.800 s 8 addr 50.782 GiB size 6.012 GiB access 0 % age 2.800 s 9 addr 34.111 GiB size 5.282 GiB access 0 % age 2.300 s 10 addr 45.489 GiB size 5.293 GiB access 0 % age 1.800 s # hottest total size: 62.000 GiB”h]”hX## damo report access --sort_regions_by temperature 0 addr 16.052 GiB size 5.985 GiB access 0 % age 5.900 s # coldest 1 addr 22.037 GiB size 6.029 GiB access 0 % age 5.300 s 2 addr 28.065 GiB size 6.045 GiB access 0 % age 5.200 s 3 addr 10.069 GiB size 5.983 GiB access 0 % age 4.500 s 4 addr 4.000 GiB size 6.069 GiB access 0 % age 4.400 s 5 addr 62.008 GiB size 3.992 GiB access 0 % age 3.700 s 6 addr 56.795 GiB size 5.213 GiB access 0 % age 3.300 s 7 addr 39.393 GiB size 6.096 GiB access 0 % age 2.800 s 8 addr 50.782 GiB size 6.012 GiB access 0 % age 2.800 s 9 addr 34.111 GiB size 5.282 GiB access 0 % age 2.300 s 10 addr 45.489 GiB size 5.293 GiB access 0 % age 1.800 s # hottest total size: 62.000 GiB”…””}”hjŽsbah}”(h]”h ]”h"]”h$]”h&]”h±h²uh1jphŸh³h K,hjQhžhubhÊ)”}”(hXrThe list shows not seemingly hot regions, and only minimum access pattern diversity. Every region has zero access frequency. The number of region is 10, which is the default ``min_nr_regions value``. Size of each region is also nearly identical. We can suspect this is because “adaptive regions adjustment†mechanism was not well working. As the guide suggested, we can get relative hotness of regions using ``age`` as the recency information. That would be better than nothing, but given the fact that the longest age is only about 6 seconds while we waited about ten minutes, it is unclear how useful this will be.”h]”(hŒ°The list shows not seemingly hot regions, and only minimum access pattern diversity. Every region has zero access frequency. The number of region is 10, which is the default ”…””}”(hjœhžhhŸNh NubhŒliteral”“”)”}”(hŒ``min_nr_regions value``”h]”hŒmin_nr_regions value”…””}”(hj¦hžhhŸNh Nubah}”(h]”h ]”h"]”h$]”h&]”uh1j¤hjœubhŒÙ. Size of each region is also nearly identical. We can suspect this is because “adaptive regions adjustment†mechanism was not well working. As the guide suggested, we can get relative hotness of regions using ”…””}”(hjœhžhhŸNh Nubj¥)”}”(hŒ``age``”h]”hŒage”…””}”(hj¸hžhhŸNh Nubah}”(h]”h ]”h"]”h$]”h&]”uh1j¤hjœubhŒÊ as the recency information. That would be better than nothing, but given the fact that the longest age is only about 6 seconds while we waited about ten minutes, it is unclear how useful this will be.”…””}”(hjœhžhhŸNh Nubeh}”(h]”h ]”h"]”h$]”h&]”uh1hÉhŸh³h K:hjQhžhubhÊ)”}”(hŒ—The temperature ranges to total size of regions of each range histogram visualization of the results also shows no interesting distribution pattern. ::”h]”hŒ”The temperature ranges to total size of regions of each range histogram visualization of the results also shows no interesting distribution pattern.”…””}”(hjÐhžhhŸNh Nubah}”(h]”h ]”h"]”h$]”h&]”uh1hÉhŸh³h KDhjQhžhubjq)”}”(hX-# damo report access --style temperature-sz-hist [-,590,000,000, -,549,000,000) 5.985 GiB |********** | [-,549,000,000, -,508,000,000) 12.074 GiB |********************| [-,508,000,000, -,467,000,000) 0 B | | [-,467,000,000, -,426,000,000) 12.052 GiB |********************| [-,426,000,000, -,385,000,000) 0 B | | [-,385,000,000, -,344,000,000) 3.992 GiB |******* | [-,344,000,000, -,303,000,000) 5.213 GiB |********* | [-,303,000,000, -,262,000,000) 12.109 GiB |********************| [-,262,000,000, -,221,000,000) 5.282 GiB |********* | [-,221,000,000, -,180,000,000) 0 B | | [-,180,000,000, -,139,000,000) 5.293 GiB |********* | total size: 62.000 GiB”h]”hX-# damo report access --style temperature-sz-hist [-,590,000,000, -,549,000,000) 5.985 GiB |********** | [-,549,000,000, -,508,000,000) 12.074 GiB |********************| [-,508,000,000, -,467,000,000) 0 B | | [-,467,000,000, -,426,000,000) 12.052 GiB |********************| [-,426,000,000, -,385,000,000) 0 B | | [-,385,000,000, -,344,000,000) 3.992 GiB |******* | [-,344,000,000, -,303,000,000) 5.213 GiB |********* | [-,303,000,000, -,262,000,000) 12.109 GiB |********************| [-,262,000,000, -,221,000,000) 5.282 GiB |********* | [-,221,000,000, -,180,000,000) 0 B | | [-,180,000,000, -,139,000,000) 5.293 GiB |********* | total size: 62.000 GiB”…””}”hjÞsbah}”(h]”h ]”h"]”h$]”h&]”h±h²uh1jphŸh³h KGhjQhžhubhÊ)”}”(hŒÛIn short, the parameters provide poor quality monitoring results for hot regions detection. According to the :ref:`guide `, this is due to the too short aggregation interval.”h]”(hŒmIn short, the parameters provide poor quality monitoring results for hot regions detection. According to the ”…””}”(hjìhžhhŸNh Nubh)”}”(hŒ::ref:`guide `”h]”hØ)”}”(hjöh]”hŒguide”…””}”(hjøhžhhŸNh Nubah}”(h]”h ]”(hãŒstd”Œstd-ref”eh"]”h$]”h&]”uh1h×hjôubah}”(h]”h ]”h"]”h$]”h&]”Œrefdoc”hðŒ refdomain”jŒreftype”Œref”Œ refexplicit”ˆŒrefwarn”ˆhöŒ+damon_design_monitoring_params_tuning_guide”uh1hhŸh³h KVhjìubhŒ4, this is due to the too short aggregation interval.”…””}”(hjìhžhhŸNh Nubeh}”(h]”h ]”h"]”h$]”h&]”uh1hÉhŸh³h KVhjQhžhubeh}”(h]”Œ%ms-100ms-intervals-too-short-interval”ah ]”h"]”Œ'5ms/100ms intervals: too short interval”ah$]”h&]”uh1h´hh¶hžhhŸh³h Kubhµ)”}”(hhh]”(hº)”}”(hŒ4100ms/2s intervals: Starts Showing Small Hot Regions”h]”hŒ4100ms/2s intervals: Starts Showing Small Hot Regions”…””}”(hj)hžhhŸNh Nubah}”(h]”h ]”h"]”h$]”h&]”uh1h¹hj&hžhhŸh³h K\ubhÊ)”}”(hŒFollowing the guide, increase the interval 20 times (100 milliseocnds and 2 seconds for sampling and aggregation intervals, respectively). ::”h]”hŒŠFollowing the guide, increase the interval 20 times (100 milliseocnds and 2 seconds for sampling and aggregation intervals, respectively).”…””}”(hj7hžhhŸNh Nubah}”(h]”h ]”h"]”h$]”h&]”uh1hÉhŸh³h K^hj&hžhubjq)”}”(hX’# damo start -s 100ms -a 2s # sleep 600 # damo record --snapshot 0 1 # damo stop # damo report access --sort_regions_by temperature 0 addr 10.180 GiB size 6.117 GiB access 0 % age 7 m 8 s # coldest 1 addr 49.275 GiB size 6.195 GiB access 0 % age 6 m 14 s 2 addr 62.421 GiB size 3.579 GiB access 0 % age 6 m 4 s 3 addr 40.154 GiB size 6.127 GiB access 0 % age 5 m 40 s 4 addr 16.296 GiB size 6.182 GiB access 0 % age 5 m 32 s 5 addr 34.254 GiB size 5.899 GiB access 0 % age 5 m 24 s 6 addr 46.281 GiB size 2.995 GiB access 0 % age 5 m 20 s 7 addr 28.420 GiB size 5.835 GiB access 0 % age 5 m 6 s 8 addr 4.000 GiB size 6.180 GiB access 0 % age 4 m 16 s 9 addr 22.478 GiB size 5.942 GiB access 0 % age 3 m 58 s 10 addr 55.470 GiB size 915.645 MiB access 0 % age 3 m 6 s 11 addr 56.364 GiB size 6.056 GiB access 0 % age 2 m 8 s 12 addr 56.364 GiB size 4.000 KiB access 95 % age 16 s 13 addr 49.275 GiB size 4.000 KiB access 100 % age 8 m 24 s # hottest total size: 62.000 GiB # damo report access --style temperature-sz-hist [-42,800,000,000, -33,479,999,000) 22.018 GiB |***************** | [-33,479,999,000, -24,159,998,000) 27.090 GiB |********************| [-24,159,998,000, -14,839,997,000) 6.836 GiB |****** | [-14,839,997,000, -5,519,996,000) 6.056 GiB |***** | [-5,519,996,000, 3,800,005,000) 4.000 KiB |* | [3,800,005,000, 13,120,006,000) 0 B | | [13,120,006,000, 22,440,007,000) 0 B | | [22,440,007,000, 31,760,008,000) 0 B | | [31,760,008,000, 41,080,009,000) 0 B | | [41,080,009,000, 50,400,010,000) 0 B | | [50,400,010,000, 59,720,011,000) 4.000 KiB |* | total size: 62.000 GiB”h]”hX’# damo start -s 100ms -a 2s # sleep 600 # damo record --snapshot 0 1 # damo stop # damo report access --sort_regions_by temperature 0 addr 10.180 GiB size 6.117 GiB access 0 % age 7 m 8 s # coldest 1 addr 49.275 GiB size 6.195 GiB access 0 % age 6 m 14 s 2 addr 62.421 GiB size 3.579 GiB access 0 % age 6 m 4 s 3 addr 40.154 GiB size 6.127 GiB access 0 % age 5 m 40 s 4 addr 16.296 GiB size 6.182 GiB access 0 % age 5 m 32 s 5 addr 34.254 GiB size 5.899 GiB access 0 % age 5 m 24 s 6 addr 46.281 GiB size 2.995 GiB access 0 % age 5 m 20 s 7 addr 28.420 GiB size 5.835 GiB access 0 % age 5 m 6 s 8 addr 4.000 GiB size 6.180 GiB access 0 % age 4 m 16 s 9 addr 22.478 GiB size 5.942 GiB access 0 % age 3 m 58 s 10 addr 55.470 GiB size 915.645 MiB access 0 % age 3 m 6 s 11 addr 56.364 GiB size 6.056 GiB access 0 % age 2 m 8 s 12 addr 56.364 GiB size 4.000 KiB access 95 % age 16 s 13 addr 49.275 GiB size 4.000 KiB access 100 % age 8 m 24 s # hottest total size: 62.000 GiB # damo report access --style temperature-sz-hist [-42,800,000,000, -33,479,999,000) 22.018 GiB |***************** | [-33,479,999,000, -24,159,998,000) 27.090 GiB |********************| [-24,159,998,000, -14,839,997,000) 6.836 GiB |****** | [-14,839,997,000, -5,519,996,000) 6.056 GiB |***** | [-5,519,996,000, 3,800,005,000) 4.000 KiB |* | [3,800,005,000, 13,120,006,000) 0 B | | [13,120,006,000, 22,440,007,000) 0 B | | [22,440,007,000, 31,760,008,000) 0 B | | [31,760,008,000, 41,080,009,000) 0 B | | [41,080,009,000, 50,400,010,000) 0 B | | [50,400,010,000, 59,720,011,000) 4.000 KiB |* | total size: 62.000 GiB”…””}”hjEsbah}”(h]”h ]”h"]”h$]”h&]”h±h²uh1jphŸh³h Kahj&hžhubhÊ)”}”(hX.DAMON found two distinct 4 KiB regions that pretty hot. The regions are also well aged. The hottest 4 KiB region was keeping the access frequency for about 8 minutes, and the coldest region was keeping no access for about 7 minutes. The distribution on the histogram also looks like having a pattern.”h]”hX.DAMON found two distinct 4 KiB regions that pretty hot. The regions are also well aged. The hottest 4 KiB region was keeping the access frequency for about 8 minutes, and the coldest region was keeping no access for about 7 minutes. The distribution on the histogram also looks like having a pattern.”…””}”(hjShžhhŸNh Nubah}”(h]”h ]”h"]”h$]”h&]”uh1hÉhŸh³h K„hj&hžhubhÊ)”}”(hŒŽEspecially, the finding of the 4 KiB regions among the 62 GiB total memory shows DAMON’s adaptive regions adjustment is working as designed.”h]”hŒŽEspecially, the finding of the 4 KiB regions among the 62 GiB total memory shows DAMON’s adaptive regions adjustment is working as designed.”…””}”(hjahžhhŸNh Nubah}”(h]”h ]”h"]”h$]”h&]”uh1hÉhŸh³h K‰hj&hžhubhÊ)”}”(hŒ­Still the number of regions is close to the ``min_nr_regions``, and sizes of cold regions are similar, though. Apparently it is improved, but it still has rooms to improve.”h]”(hŒ,Still the number of regions is close to the ”…””}”(hjohžhhŸNh Nubj¥)”}”(hŒ``min_nr_regions``”h]”hŒmin_nr_regions”…””}”(hjwhžhhŸNh Nubah}”(h]”h ]”h"]”h$]”h&]”uh1j¤hjoubhŒo, and sizes of cold regions are similar, though. Apparently it is improved, but it still has rooms to improve.”…””}”(hjohžhhŸNh Nubeh}”(h]”h ]”h"]”h$]”h&]”uh1hÉhŸh³h KŒhj&hžhubeh}”(h]”Œ0ms-2s-intervals-starts-showing-small-hot-regions”ah ]”h"]”Œ4100ms/2s intervals: starts showing small hot regions”ah$]”h&]”uh1h´hh¶hžhhŸh³h K\ubhµ)”}”(hhh]”(hº)”}”(hŒ+400ms/8s intervals: Pretty Improved Results”h]”hŒ+400ms/8s intervals: Pretty Improved Results”…””}”(hjšhžhhŸNh Nubah}”(h]”h ]”h"]”h$]”h&]”uh1h¹hj—hžhhŸh³h K‘ubhÊ)”}”(hŒ{Increase the intervals four times (400 milliseconds and 8 seconds for sampling and aggregation intervals, respectively). ::”h]”hŒxIncrease the intervals four times (400 milliseconds and 8 seconds for sampling and aggregation intervals, respectively).”…””}”(hj¨hžhhŸNh Nubah}”(h]”h ]”h"]”h$]”h&]”uh1hÉhŸh³h K“hj—hžhubjq)”}”(hXS# damo start -s 400ms -a 8s # sleep 600 # damo record --snapshot 0 1 # damo stop # damo report access --sort_regions_by temperature 0 addr 64.492 GiB size 1.508 GiB access 0 % age 6 m 48 s # coldest 1 addr 21.749 GiB size 5.674 GiB access 0 % age 6 m 8 s 2 addr 27.422 GiB size 5.801 GiB access 0 % age 6 m 3 addr 49.431 GiB size 8.675 GiB access 0 % age 5 m 28 s 4 addr 33.223 GiB size 5.645 GiB access 0 % age 5 m 12 s 5 addr 58.321 GiB size 6.170 GiB access 0 % age 5 m 4 s [...] 25 addr 6.615 GiB size 297.531 MiB access 15 % age 0 ns 26 addr 9.513 GiB size 12.000 KiB access 20 % age 0 ns 27 addr 9.511 GiB size 108.000 KiB access 25 % age 0 ns 28 addr 9.513 GiB size 20.000 KiB access 25 % age 0 ns 29 addr 9.511 GiB size 12.000 KiB access 30 % age 0 ns 30 addr 9.520 GiB size 4.000 KiB access 40 % age 0 ns [...] 41 addr 9.520 GiB size 4.000 KiB access 80 % age 56 s 42 addr 9.511 GiB size 12.000 KiB access 100 % age 6 m 16 s 43 addr 58.321 GiB size 4.000 KiB access 100 % age 6 m 24 s 44 addr 9.512 GiB size 4.000 KiB access 100 % age 6 m 48 s 45 addr 58.106 GiB size 4.000 KiB access 100 % age 6 m 48 s # hottest total size: 62.000 GiB # damo report access --style temperature-sz-hist [-40,800,000,000, -32,639,999,000) 21.657 GiB |********************| [-32,639,999,000, -24,479,998,000) 17.938 GiB |***************** | [-24,479,998,000, -16,319,997,000) 16.885 GiB |**************** | [-16,319,997,000, -8,159,996,000) 586.879 MiB |* | [-8,159,996,000, 5,000) 4.946 GiB |***** | [5,000, 8,160,006,000) 260.000 KiB |* | [8,160,006,000, 16,320,007,000) 0 B | | [16,320,007,000, 24,480,008,000) 0 B | | [24,480,008,000, 32,640,009,000) 0 B | | [32,640,009,000, 40,800,010,000) 16.000 KiB |* | [40,800,010,000, 48,960,011,000) 8.000 KiB |* | total size: 62.000 GiB”h]”hXS# damo start -s 400ms -a 8s # sleep 600 # damo record --snapshot 0 1 # damo stop # damo report access --sort_regions_by temperature 0 addr 64.492 GiB size 1.508 GiB access 0 % age 6 m 48 s # coldest 1 addr 21.749 GiB size 5.674 GiB access 0 % age 6 m 8 s 2 addr 27.422 GiB size 5.801 GiB access 0 % age 6 m 3 addr 49.431 GiB size 8.675 GiB access 0 % age 5 m 28 s 4 addr 33.223 GiB size 5.645 GiB access 0 % age 5 m 12 s 5 addr 58.321 GiB size 6.170 GiB access 0 % age 5 m 4 s [...] 25 addr 6.615 GiB size 297.531 MiB access 15 % age 0 ns 26 addr 9.513 GiB size 12.000 KiB access 20 % age 0 ns 27 addr 9.511 GiB size 108.000 KiB access 25 % age 0 ns 28 addr 9.513 GiB size 20.000 KiB access 25 % age 0 ns 29 addr 9.511 GiB size 12.000 KiB access 30 % age 0 ns 30 addr 9.520 GiB size 4.000 KiB access 40 % age 0 ns [...] 41 addr 9.520 GiB size 4.000 KiB access 80 % age 56 s 42 addr 9.511 GiB size 12.000 KiB access 100 % age 6 m 16 s 43 addr 58.321 GiB size 4.000 KiB access 100 % age 6 m 24 s 44 addr 9.512 GiB size 4.000 KiB access 100 % age 6 m 48 s 45 addr 58.106 GiB size 4.000 KiB access 100 % age 6 m 48 s # hottest total size: 62.000 GiB # damo report access --style temperature-sz-hist [-40,800,000,000, -32,639,999,000) 21.657 GiB |********************| [-32,639,999,000, -24,479,998,000) 17.938 GiB |***************** | [-24,479,998,000, -16,319,997,000) 16.885 GiB |**************** | [-16,319,997,000, -8,159,996,000) 586.879 MiB |* | [-8,159,996,000, 5,000) 4.946 GiB |***** | [5,000, 8,160,006,000) 260.000 KiB |* | [8,160,006,000, 16,320,007,000) 0 B | | [16,320,007,000, 24,480,008,000) 0 B | | [24,480,008,000, 32,640,009,000) 0 B | | [32,640,009,000, 40,800,010,000) 16.000 KiB |* | [40,800,010,000, 48,960,011,000) 8.000 KiB |* | total size: 62.000 GiB”…””}”hj¶sbah}”(h]”h ]”h"]”h$]”h&]”h±h²uh1jphŸh³h K–hj—hžhubhÊ)”}”(hX.The number of regions having different access patterns has significantly increased. Size of each region is also more varied. Total size of non-zero access frequency regions is also significantly increased. Maybe this is already good enough to make some meaningful memory management efficiency changes.”h]”hX.The number of regions having different access patterns has significantly increased. Size of each region is also more varied. Total size of non-zero access frequency regions is also significantly increased. Maybe this is already good enough to make some meaningful memory management efficiency changes.”…””}”(hjÄhžhhŸNh Nubah}”(h]”h ]”h"]”h$]”h&]”uh1hÉhŸh³h K¾hj—hžhubeh}”(h]”Œ'ms-8s-intervals-pretty-improved-results”ah ]”h"]”Œ+400ms/8s intervals: pretty improved results”ah$]”h&]”uh1h´hh¶hžhhŸh³h K‘ubhµ)”}”(hhh]”(hº)”}”(hŒ!800ms/16s intervals: Another bias”h]”hŒ!800ms/16s intervals: Another bias”…””}”(hjÝhžhhŸNh Nubah}”(h]”h ]”h"]”h$]”h&]”uh1h¹hjÚhžhhŸh³h KÄubhÊ)”}”(hŒéFurther double the intervals (800 milliseconds and 16 seconds for sampling and aggregation intervals, respectively). The results is more improved for the hot regions detection, but starts looking degrading cold regions detection. ::”h]”hŒæFurther double the intervals (800 milliseconds and 16 seconds for sampling and aggregation intervals, respectively). The results is more improved for the hot regions detection, but starts looking degrading cold regions detection.”…””}”(hjëhžhhŸNh Nubah}”(h]”h ]”h"]”h$]”h&]”uh1hÉhŸh³h KÆhjÚhžhubjq)”}”(hX0# damo start -s 800ms -a 16s # sleep 600 # damo record --snapshot 0 1 # damo stop # damo report access --sort_regions_by temperature 0 addr 64.781 GiB size 1.219 GiB access 0 % age 4 m 48 s 1 addr 24.505 GiB size 2.475 GiB access 0 % age 4 m 16 s 2 addr 26.980 GiB size 504.273 MiB access 0 % age 4 m 3 addr 29.443 GiB size 2.462 GiB access 0 % age 4 m 4 addr 37.264 GiB size 5.645 GiB access 0 % age 4 m 5 addr 31.905 GiB size 5.359 GiB access 0 % age 3 m 44 s [...] 20 addr 8.711 GiB size 40.000 KiB access 5 % age 2 m 40 s 21 addr 27.473 GiB size 1.970 GiB access 5 % age 4 m 22 addr 48.185 GiB size 4.625 GiB access 5 % age 4 m 23 addr 47.304 GiB size 902.117 MiB access 10 % age 4 m 24 addr 8.711 GiB size 4.000 KiB access 100 % age 4 m 25 addr 20.793 GiB size 3.713 GiB access 5 % age 4 m 16 s 26 addr 8.773 GiB size 4.000 KiB access 100 % age 4 m 16 s total size: 62.000 GiB # damo report access --style temperature-sz-hist [-28,800,000,000, -23,359,999,000) 12.294 GiB |***************** | [-23,359,999,000, -17,919,998,000) 9.753 GiB |************* | [-17,919,998,000, -12,479,997,000) 15.131 GiB |********************| [-12,479,997,000, -7,039,996,000) 0 B | | [-7,039,996,000, -1,599,995,000) 7.506 GiB |********** | [-1,599,995,000, 3,840,006,000) 6.127 GiB |********* | [3,840,006,000, 9,280,007,000) 0 B | | [9,280,007,000, 14,720,008,000) 136.000 KiB |* | [14,720,008,000, 20,160,009,000) 40.000 KiB |* | [20,160,009,000, 25,600,010,000) 11.188 GiB |*************** | [25,600,010,000, 31,040,011,000) 4.000 KiB |* | total size: 62.000 GiB”h]”hX0# damo start -s 800ms -a 16s # sleep 600 # damo record --snapshot 0 1 # damo stop # damo report access --sort_regions_by temperature 0 addr 64.781 GiB size 1.219 GiB access 0 % age 4 m 48 s 1 addr 24.505 GiB size 2.475 GiB access 0 % age 4 m 16 s 2 addr 26.980 GiB size 504.273 MiB access 0 % age 4 m 3 addr 29.443 GiB size 2.462 GiB access 0 % age 4 m 4 addr 37.264 GiB size 5.645 GiB access 0 % age 4 m 5 addr 31.905 GiB size 5.359 GiB access 0 % age 3 m 44 s [...] 20 addr 8.711 GiB size 40.000 KiB access 5 % age 2 m 40 s 21 addr 27.473 GiB size 1.970 GiB access 5 % age 4 m 22 addr 48.185 GiB size 4.625 GiB access 5 % age 4 m 23 addr 47.304 GiB size 902.117 MiB access 10 % age 4 m 24 addr 8.711 GiB size 4.000 KiB access 100 % age 4 m 25 addr 20.793 GiB size 3.713 GiB access 5 % age 4 m 16 s 26 addr 8.773 GiB size 4.000 KiB access 100 % age 4 m 16 s total size: 62.000 GiB # damo report access --style temperature-sz-hist [-28,800,000,000, -23,359,999,000) 12.294 GiB |***************** | [-23,359,999,000, -17,919,998,000) 9.753 GiB |************* | [-17,919,998,000, -12,479,997,000) 15.131 GiB |********************| [-12,479,997,000, -7,039,996,000) 0 B | | [-7,039,996,000, -1,599,995,000) 7.506 GiB |********** | [-1,599,995,000, 3,840,006,000) 6.127 GiB |********* | [3,840,006,000, 9,280,007,000) 0 B | | [9,280,007,000, 14,720,008,000) 136.000 KiB |* | [14,720,008,000, 20,160,009,000) 40.000 KiB |* | [20,160,009,000, 25,600,010,000) 11.188 GiB |*************** | [25,600,010,000, 31,040,011,000) 4.000 KiB |* | total size: 62.000 GiB”…””}”hjùsbah}”(h]”h ]”h"]”h$]”h&]”h±h²uh1jphŸh³h KÊhjÚhžhubhÊ)”}”(hŒìIt found more non-zero access frequency regions. The number of regions is still much higher than the ``min_nr_regions``, but it is reduced from that of the previous setup. And apparently the distribution seems bit biased to hot regions.”h]”(hŒeIt found more non-zero access frequency regions. The number of regions is still much higher than the ”…””}”(hjhžhhŸNh Nubj¥)”}”(hŒ``min_nr_regions``”h]”hŒmin_nr_regions”…””}”(hjhžhhŸNh Nubah}”(h]”h ]”h"]”h$]”h&]”uh1j¤hjubhŒu, but it is reduced from that of the previous setup. And apparently the distribution seems bit biased to hot regions.”…””}”(hjhžhhŸNh Nubeh}”(h]”h ]”h"]”h$]”h&]”uh1hÉhŸh³h KíhjÚhžhubeh}”(h]”Œms-16s-intervals-another-bias”ah ]”h"]”Œ!800ms/16s intervals: another bias”ah$]”h&]”uh1h´hh¶hžhhŸh³h KÄubhµ)”}”(hhh]”(hº)”}”(hŒ Conclusion”h]”hŒ Conclusion”…””}”(hj2hžhhŸNh Nubah}”(h]”h ]”h"]”h$]”h&]”uh1h¹hj/hžhhŸh³h KóubhÊ)”}”(hŒ¢With the above experimental tuning results, we can conclude the theory and the guide makes sense to at least this workload, and could be applied to similar cases.”h]”hŒ¢With the above experimental tuning results, we can conclude the theory and the guide makes sense to at least this workload, and could be applied to similar cases.”…””}”(hj@hžhhŸNh Nubah}”(h]”h ]”h"]”h$]”h&]”uh1hÉhŸh³h Kõhj/hžhubeh}”(h]”Œ conclusion”ah ]”h"]”Œ conclusion”ah$]”h&]”uh1h´hh¶hžhhŸh³h Kóubeh}”(h]”Œ1damon-moniting-interval-parameters-tuning-example”ah ]”h"]”Œ1damon moniting interval parameters tuning example”ah$]”h&]”uh1h´hhhžhhŸh³h Kubeh}”(h]”h ]”h"]”h$]”h&]”Œsource”h³uh1hŒcurrent_source”NŒ current_line”NŒsettings”Œdocutils.frontend”ŒValues”“”)”}”(h¹NŒ generator”NŒ datestamp”NŒ source_link”NŒ source_url”NŒ toc_backlinks”Œentry”Œfootnote_backlinks”KŒ sectnum_xform”KŒstrip_comments”NŒstrip_elements_with_classes”NŒ strip_classes”NŒ report_level”KŒ halt_level”KŒexit_status_level”KŒdebug”NŒwarning_stream”NŒ traceback”ˆŒinput_encoding”Œ utf-8-sig”Œinput_encoding_error_handler”Œstrict”Œoutput_encoding”Œutf-8”Œoutput_encoding_error_handler”jŒerror_encoding”Œutf-8”Œerror_encoding_error_handler”Œbackslashreplace”Œ language_code”Œen”Œrecord_dependencies”NŒconfig”NŒ id_prefix”hŒauto_id_prefix”Œid”Œ dump_settings”NŒdump_internals”NŒdump_transforms”NŒdump_pseudo_xml”NŒexpose_internals”NŒstrict_visitor”NŒ_disable_config”NŒ_source”h³Œ _destination”NŒ _config_files”]”Œ7/var/lib/git/docbuild/linux/Documentation/docutils.conf”aŒfile_insertion_enabled”ˆŒ raw_enabled”KŒline_length_limit”M'Œpep_references”NŒ pep_base_url”Œhttps://peps.python.org/”Œpep_file_url_template”Œpep-%04d”Œrfc_references”NŒ rfc_base_url”Œ&https://datatracker.ietf.org/doc/html/”Œ tab_width”KŒtrim_footnote_reference_space”‰Œsyntax_highlight”Œlong”Œ smart_quotes”ˆŒsmartquotes_locales”]”Œcharacter_level_inline_markup”‰Œdoctitle_xform”‰Œ docinfo_xform”KŒsectsubtitle_xform”‰Œ image_loading”Œlink”Œembed_stylesheet”‰Œcloak_email_addresses”ˆŒsection_self_link”‰Œenv”NubŒreporter”NŒindirect_targets”]”Œsubstitution_defs”}”Œsubstitution_names”}”Œrefnames”}”Œrefids”}”Œnameids”}”(j[jXjNjKj:j7j#j j”j‘j×jÔj,j)jSjPuŒ nametypes”}”(j[‰jN‰j:ˆj#‰j”‰j׉j,‰jS‰uh}”(jXh¶jKjj7j1j jQj‘j&jÔj—j)jÚjPj/uŒ footnote_refs”}”Œ citation_refs”}”Œ autofootnotes”]”Œautofootnote_refs”]”Œsymbol_footnotes”]”Œsymbol_footnote_refs”]”Œ footnotes”]”Œ citations”]”Œautofootnote_start”KŒsymbol_footnote_start”KŒ id_counter”Œ collections”ŒCounter”“”}”…”R”Œparse_messages”]”Œtransform_messages”]”Œ transformer”NŒ include_log”]”Œ decoration”Nhžhub.